Overview

Dataset statistics

Number of variables19
Number of observations1267
Missing cells1514
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory188.2 KiB
Average record size in memory152.1 B

Variable types

Numeric14
Categorical4
Boolean1

Alerts

Aneu_neck is highly overall correlated with Aneu_width and 9 other fieldsHigh correlation
Aneu_width is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
Aneu_height is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
Aneu_volume is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_count is highly overall correlated with Aneu_neck and 8 other fieldsHigh correlation
coil_length1 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_size1 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_size2 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_length2 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_size3 is highly overall correlated with Aneu_neck and 10 other fieldsHigh correlation
coil_length3 is highly overall correlated with Aneu_neck and 9 other fieldsHigh correlation
Aneu_width_label is highly overall correlated with Aneu_width and 8 other fieldsHigh correlation
Is_bleb is highly imbalanced (68.5%)Imbalance
VER has 1096 (86.5%) missing valuesMissing
coil_size2 has 53 (4.2%) missing valuesMissing
coil_length2 has 53 (4.2%) missing valuesMissing
coil_size3 has 156 (12.3%) missing valuesMissing
coil_length3 has 156 (12.3%) missing valuesMissing
ID has unique valuesUnique

Reproduction

Analysis started2023-09-16 06:34:47.660039
Analysis finished2023-09-16 06:35:24.717153
Duration37.06 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct1267
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3740.7419
Minimum3026
Maximum4434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:24.813783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3026
5-th percentile3101.3
Q13387.5
median3746
Q34098.5
95-th percentile4358.7
Maximum4434
Range1408
Interquartile range (IQR)711

Descriptive statistics

Standard deviation405.9595
Coefficient of variation (CV)0.10852379
Kurtosis-1.2155041
Mean3740.7419
Median Absolute Deviation (MAD)355
Skewness-0.040260283
Sum4739520
Variance164803.12
MonotonicityNot monotonic
2023-09-16T15:35:25.012914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3027 1
 
0.1%
4201 1
 
0.1%
4211 1
 
0.1%
4210 1
 
0.1%
4208 1
 
0.1%
4207 1
 
0.1%
4206 1
 
0.1%
4205 1
 
0.1%
4204 1
 
0.1%
4200 1
 
0.1%
Other values (1257) 1257
99.2%
ValueCountFrequency (%)
3026 1
0.1%
3027 1
0.1%
3030 1
0.1%
3031 1
0.1%
3034 1
0.1%
3035 1
0.1%
3037 1
0.1%
3038 1
0.1%
3039 1
0.1%
3040 1
0.1%
ValueCountFrequency (%)
4434 1
0.1%
4433 1
0.1%
4432 1
0.1%
4431 1
0.1%
4430 1
0.1%
4427 1
0.1%
4425 1
0.1%
4424 1
0.1%
4423 1
0.1%
4422 1
0.1%

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
woman
929 
man
338 

Length

Max length5
Median length5
Mean length4.4664562
Min length3

Characters and Unicode

Total characters5659
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwoman
2nd rowwoman
3rd rowman
4th rowwoman
5th rowwoman

Common Values

ValueCountFrequency (%)
woman 929
73.3%
man 338
 
26.7%

Length

2023-09-16T15:35:25.212084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:35:25.383222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
woman 929
73.3%
man 338
 
26.7%

Most occurring characters

ValueCountFrequency (%)
m 1267
22.4%
a 1267
22.4%
n 1267
22.4%
w 929
16.4%
o 929
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5659
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1267
22.4%
a 1267
22.4%
n 1267
22.4%
w 929
16.4%
o 929
16.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5659
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1267
22.4%
a 1267
22.4%
n 1267
22.4%
w 929
16.4%
o 929
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1267
22.4%
a 1267
22.4%
n 1267
22.4%
w 929
16.4%
o 929
16.4%

Age
Real number (ℝ)

Distinct63
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.470403
Minimum22
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:25.543551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile40
Q152
median61
Q370
95-th percentile78
Maximum89
Range67
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.692061
Coefficient of variation (CV)0.1933518
Kurtosis-0.39385103
Mean60.470403
Median Absolute Deviation (MAD)9
Skewness-0.25657886
Sum76616
Variance136.7043
MonotonicityNot monotonic
2023-09-16T15:35:25.737134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 51
 
4.0%
64 47
 
3.7%
70 45
 
3.6%
60 44
 
3.5%
50 43
 
3.4%
58 42
 
3.3%
62 41
 
3.2%
71 39
 
3.1%
63 38
 
3.0%
67 37
 
2.9%
Other values (53) 840
66.3%
ValueCountFrequency (%)
22 1
 
0.1%
25 2
0.2%
29 4
0.3%
30 1
 
0.1%
31 1
 
0.1%
32 2
0.2%
33 2
0.2%
34 4
0.3%
35 4
0.3%
36 3
0.2%
ValueCountFrequency (%)
89 2
 
0.2%
88 1
 
0.1%
87 4
 
0.3%
86 1
 
0.1%
85 2
 
0.2%
84 4
 
0.3%
83 10
0.8%
82 4
 
0.3%
81 6
0.5%
80 6
0.5%

Aneu_location
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
ICA
709 
ACA
263 
MCA
159 
BA
105 
VA
 
31

Length

Max length3
Median length3
Mean length2.8926598
Min length2

Characters and Unicode

Total characters3665
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBA
2nd rowICA
3rd rowACA
4th rowICA
5th rowACA

Common Values

ValueCountFrequency (%)
ICA 709
56.0%
ACA 263
 
20.8%
MCA 159
 
12.5%
BA 105
 
8.3%
VA 31
 
2.4%

Length

2023-09-16T15:35:25.925893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:35:26.103142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ica 709
56.0%
aca 263
 
20.8%
mca 159
 
12.5%
ba 105
 
8.3%
va 31
 
2.4%

Most occurring characters

ValueCountFrequency (%)
A 1530
41.7%
C 1131
30.9%
I 709
19.3%
M 159
 
4.3%
B 105
 
2.9%
V 31
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3665
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1530
41.7%
C 1131
30.9%
I 709
19.3%
M 159
 
4.3%
B 105
 
2.9%
V 31
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3665
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1530
41.7%
C 1131
30.9%
I 709
19.3%
M 159
 
4.3%
B 105
 
2.9%
V 31
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1530
41.7%
C 1131
30.9%
I 709
19.3%
M 159
 
4.3%
B 105
 
2.9%
V 31
 
0.8%

Aneu_neck
Real number (ℝ)

Distinct349
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.126543
Minimum0.99
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:26.291819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile1.583
Q12.3
median3
Q33.75
95-th percentile5.335
Maximum8.5
Range7.51
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation1.1521134
Coefficient of variation (CV)0.36849434
Kurtosis1.2312162
Mean3.126543
Median Absolute Deviation (MAD)0.73
Skewness0.94360738
Sum3961.33
Variance1.3273653
MonotonicityNot monotonic
2023-09-16T15:35:26.493882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 100
 
7.9%
2.5 83
 
6.6%
2 73
 
5.8%
3.5 65
 
5.1%
4 47
 
3.7%
4.5 31
 
2.4%
5 25
 
2.0%
1.5 24
 
1.9%
5.5 12
 
0.9%
2.4 11
 
0.9%
Other values (339) 796
62.8%
ValueCountFrequency (%)
0.99 1
 
0.1%
1 5
0.4%
1.06 1
 
0.1%
1.07 1
 
0.1%
1.1 2
 
0.2%
1.13 1
 
0.1%
1.2 4
0.3%
1.26 1
 
0.1%
1.31 1
 
0.1%
1.32 1
 
0.1%
ValueCountFrequency (%)
8.5 1
 
0.1%
8 3
0.2%
7.51 1
 
0.1%
7.4 1
 
0.1%
7.2 1
 
0.1%
7.1 1
 
0.1%
7 2
0.2%
6.86 1
 
0.1%
6.5 1
 
0.1%
6.43 1
 
0.1%

Aneu_width
Real number (ℝ)

Distinct423
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4423433
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:26.704649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q13.065
median4.1
Q35.33
95-th percentile7.994
Maximum11
Range10
Interquartile range (IQR)2.265

Descriptive statistics

Standard deviation1.7200093
Coefficient of variation (CV)0.38718513
Kurtosis0.63033345
Mean4.4423433
Median Absolute Deviation (MAD)1.1
Skewness0.90813881
Sum5628.449
Variance2.958432
MonotonicityNot monotonic
2023-09-16T15:35:26.901032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 69
 
5.4%
4 61
 
4.8%
3.5 57
 
4.5%
4.5 56
 
4.4%
5 49
 
3.9%
6 33
 
2.6%
2.5 28
 
2.2%
5.5 27
 
2.1%
7 24
 
1.9%
2 22
 
1.7%
Other values (413) 841
66.4%
ValueCountFrequency (%)
1 1
 
0.1%
1.2 1
 
0.1%
1.3 1
 
0.1%
1.43 1
 
0.1%
1.5 3
0.2%
1.55 1
 
0.1%
1.56 1
 
0.1%
1.58 1
 
0.1%
1.63 1
 
0.1%
1.66 1
 
0.1%
ValueCountFrequency (%)
11 1
0.1%
10.5 2
0.2%
10.3 1
0.1%
10.23 1
0.1%
10 1
0.1%
9.9 1
0.1%
9.83 1
0.1%
9.77 1
0.1%
9.75 1
0.1%
9.6 2
0.2%

Aneu_height
Real number (ℝ)

Distinct416
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7347277
Minimum1.5
Maximum11.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:27.101321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.5
Q13.5
median4.5
Q35.58
95-th percentile8
Maximum11.96
Range10.46
Interquartile range (IQR)2.08

Descriptive statistics

Standard deviation1.7171262
Coefficient of variation (CV)0.3626663
Kurtosis1.110062
Mean4.7347277
Median Absolute Deviation (MAD)1
Skewness0.99786788
Sum5998.9
Variance2.9485223
MonotonicityNot monotonic
2023-09-16T15:35:27.304800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 65
 
5.1%
5 62
 
4.9%
4.5 55
 
4.3%
3.5 50
 
3.9%
3 43
 
3.4%
5.5 42
 
3.3%
6 35
 
2.8%
7 33
 
2.6%
6.5 18
 
1.4%
8 17
 
1.3%
Other values (406) 847
66.9%
ValueCountFrequency (%)
1.5 1
 
0.1%
1.53 1
 
0.1%
1.55 1
 
0.1%
1.59 1
 
0.1%
1.7 2
 
0.2%
1.75 1
 
0.1%
1.85 1
 
0.1%
1.92 1
 
0.1%
2 6
0.5%
2.01 1
 
0.1%
ValueCountFrequency (%)
11.96 1
 
0.1%
11.5 1
 
0.1%
11 2
 
0.2%
10.8 1
 
0.1%
10.72 1
 
0.1%
10.55 1
 
0.1%
10.52 1
 
0.1%
10.5 1
 
0.1%
10.1 1
 
0.1%
10 6
0.5%

Aneu_volume
Real number (ℝ)

Distinct973
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.872032
Minimum1.0471976
Maximum519.80268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:27.507202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.0471976
5-th percentile7.6533687
Q119.257911
median39.516261
Q379.225882
95-th percentile235.61945
Maximum519.80268
Range518.75549
Interquartile range (IQR)59.967971

Descriptive statistics

Standard deviation80.236035
Coefficient of variation (CV)1.1821664
Kurtosis7.993526
Mean67.872032
Median Absolute Deviation (MAD)24.195238
Skewness2.6068907
Sum85993.864
Variance6437.8214
MonotonicityNot monotonic
2023-09-16T15:35:27.868012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.01437603 16
 
1.3%
16.49336143 16
 
1.3%
33.51032164 14
 
1.1%
37.69911184 13
 
1.0%
18.84955592 13
 
1.0%
25.65634 11
 
0.9%
21.20575041 10
 
0.8%
131.9468915 9
 
0.7%
9.817477042 9
 
0.7%
71.99483164 9
 
0.7%
Other values (963) 1147
90.5%
ValueCountFrequency (%)
1.047197551 1
0.1%
1.281769803 1
0.1%
1.327322896 1
0.1%
2.128458868 1
0.1%
2.474004215 1
0.1%
2.566209959 1
0.1%
2.580404198 1
0.1%
2.717893906 1
0.1%
2.836433004 1
0.1%
2.945243113 2
0.2%
ValueCountFrequency (%)
519.8026845 1
0.1%
518.3109516 1
0.1%
506.8436148 1
0.1%
495.2573556 1
0.1%
487.7322595 1
0.1%
476.3000755 1
0.1%
461.8141201 1
0.1%
458.4212 1
0.1%
442.6080814 1
0.1%
437.5066667 1
0.1%

Adj_tech
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
Stent assist
447 
BAT
446 
Simple
300 
Double cathe
73 
Triple cathe
 
1

Length

Max length12
Median length6
Mean length7.4112076
Min length3

Characters and Unicode

Total characters9390
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSimple
2nd rowBAT
3rd rowSimple
4th rowSimple
5th rowSimple

Common Values

ValueCountFrequency (%)
Stent assist 447
35.3%
BAT 446
35.2%
Simple 300
23.7%
Double cathe 73
 
5.8%
Triple cathe 1
 
0.1%

Length

2023-09-16T15:35:28.043380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:35:28.215122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
stent 447
25.0%
assist 447
25.0%
bat 446
24.9%
simple 300
16.8%
cathe 74
 
4.1%
double 73
 
4.1%
triple 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 1415
15.1%
s 1341
14.3%
e 895
9.5%
i 748
8.0%
S 747
8.0%
521
 
5.5%
a 521
 
5.5%
T 447
 
4.8%
n 447
 
4.8%
B 446
 
4.7%
Other values (11) 1862
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6710
71.5%
Uppercase Letter 2159
 
23.0%
Space Separator 521
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1415
21.1%
s 1341
20.0%
e 895
13.3%
i 748
11.1%
a 521
 
7.8%
n 447
 
6.7%
l 374
 
5.6%
p 301
 
4.5%
m 300
 
4.5%
c 74
 
1.1%
Other values (5) 294
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
S 747
34.6%
T 447
20.7%
B 446
20.7%
A 446
20.7%
D 73
 
3.4%
Space Separator
ValueCountFrequency (%)
521
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8869
94.5%
Common 521
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1415
16.0%
s 1341
15.1%
e 895
10.1%
i 748
8.4%
S 747
8.4%
a 521
 
5.9%
T 447
 
5.0%
n 447
 
5.0%
B 446
 
5.0%
A 446
 
5.0%
Other values (10) 1416
16.0%
Common
ValueCountFrequency (%)
521
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1415
15.1%
s 1341
14.3%
e 895
9.5%
i 748
8.0%
S 747
8.0%
521
 
5.5%
a 521
 
5.5%
T 447
 
4.8%
n 447
 
4.8%
B 446
 
4.7%
Other values (11) 1862
19.8%

Is_bleb
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
False
1195 
True
 
72
ValueCountFrequency (%)
False 1195
94.3%
True 72
 
5.7%
2023-09-16T15:35:28.389741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

VER
Real number (ℝ)

Distinct141
Distinct (%)82.5%
Missing1096
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean34.423918
Minimum12.89
Maximum70.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:28.539274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.89
5-th percentile19.8
Q127.4
median33
Q339.04
95-th percentile55.705
Maximum70.58
Range57.69
Interquartile range (IQR)11.64

Descriptive statistics

Standard deviation10.833136
Coefficient of variation (CV)0.31469794
Kurtosis1.2023413
Mean34.423918
Median Absolute Deviation (MAD)5.9
Skewness0.9085764
Sum5886.49
Variance117.35684
MonotonicityNot monotonic
2023-09-16T15:35:28.729035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.8 4
 
0.3%
24.5 3
 
0.2%
25.2 3
 
0.2%
36.5 3
 
0.2%
35 2
 
0.2%
32 2
 
0.2%
34.1 2
 
0.2%
26.5 2
 
0.2%
27.5 2
 
0.2%
32.5 2
 
0.2%
Other values (131) 146
 
11.5%
(Missing) 1096
86.5%
ValueCountFrequency (%)
12.89 1
0.1%
14.2 1
0.1%
14.21 1
0.1%
15 1
0.1%
16.1 1
0.1%
17.3 1
0.1%
17.5 1
0.1%
19.4 1
0.1%
19.6 1
0.1%
20 1
0.1%
ValueCountFrequency (%)
70.58 1
0.1%
69.7 1
0.1%
66.4 1
0.1%
64.5 1
0.1%
60.8 1
0.1%
60.4 1
0.1%
59.5 1
0.1%
58.8 1
0.1%
56.7 1
0.1%
54.71 1
0.1%

coil_count
Real number (ℝ)

Distinct30
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4546172
Minimum1
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:28.913267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile14
Maximum58
Range57
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.4382849
Coefficient of variation (CV)0.68761397
Kurtosis18.547463
Mean6.4546172
Median Absolute Deviation (MAD)2
Skewness2.7836123
Sum8178
Variance19.698373
MonotonicityNot monotonic
2023-09-16T15:35:29.075629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 159
12.5%
4 159
12.5%
5 149
11.8%
6 146
11.5%
7 113
8.9%
2 103
8.1%
8 95
7.5%
9 71
5.6%
10 54
 
4.3%
1 53
 
4.2%
Other values (20) 165
13.0%
ValueCountFrequency (%)
1 53
 
4.2%
2 103
8.1%
3 159
12.5%
4 159
12.5%
5 149
11.8%
6 146
11.5%
7 113
8.9%
8 95
7.5%
9 71
5.6%
10 54
 
4.3%
ValueCountFrequency (%)
58 1
 
0.1%
34 1
 
0.1%
33 1
 
0.1%
30 1
 
0.1%
28 2
0.2%
27 1
 
0.1%
25 2
0.2%
24 2
0.2%
22 1
 
0.1%
21 4
0.3%

Aneu_width_label
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.0
852 
1.0
285 
2.0
130 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3801
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 852
67.2%
1.0 285
 
22.5%
2.0 130
 
10.3%

Length

2023-09-16T15:35:29.246705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T15:35:29.408332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 852
67.2%
1.0 285
 
22.5%
2.0 130
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 2119
55.7%
. 1267
33.3%
1 285
 
7.5%
2 130
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2534
66.7%
Other Punctuation 1267
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2119
83.6%
1 285
 
11.2%
2 130
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 1267
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3801
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2119
55.7%
. 1267
33.3%
1 285
 
7.5%
2 130
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2119
55.7%
. 1267
33.3%
1 285
 
7.5%
2 130
 
3.4%

coil_length1
Real number (ℝ)

Distinct39
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.746961
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:29.572036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q315
95-th percentile25.7
Maximum45
Range44
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8020683
Coefficient of variation (CV)0.63292944
Kurtosis1.9032428
Mean10.746961
Median Absolute Deviation (MAD)2
Skewness1.4482574
Sum13616.4
Variance46.268133
MonotonicityNot monotonic
2023-09-16T15:35:29.748623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
8 251
19.8%
6 216
17.0%
10 186
14.7%
15 131
10.3%
4 121
9.6%
20 92
 
7.3%
30 52
 
4.1%
12 39
 
3.1%
3 31
 
2.4%
25 21
 
1.7%
Other values (29) 127
10.0%
ValueCountFrequency (%)
1 1
 
0.1%
2 18
 
1.4%
2.5 2
 
0.2%
3 31
 
2.4%
3.5 4
 
0.3%
4 121
9.6%
4.5 3
 
0.2%
5 4
 
0.3%
5.4 3
 
0.2%
6 216
17.0%
ValueCountFrequency (%)
45 1
 
0.1%
40 2
 
0.2%
30 52
4.1%
29 3
 
0.2%
28 3
 
0.2%
27 1
 
0.1%
26 2
 
0.2%
25 21
1.7%
24 4
 
0.3%
23 1
 
0.1%

coil_size1
Real number (ℝ)

Distinct17
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4368587
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:29.905323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8243241
Coefficient of variation (CV)0.41117472
Kurtosis1.7326692
Mean4.4368587
Median Absolute Deviation (MAD)1
Skewness1.1379081
Sum5621.5
Variance3.3281585
MonotonicityNot monotonic
2023-09-16T15:35:30.059301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 245
19.3%
4 235
18.5%
5 217
17.1%
6 134
10.6%
3.5 98
 
7.7%
2.5 87
 
6.9%
7 75
 
5.9%
2 61
 
4.8%
8 42
 
3.3%
9 22
 
1.7%
Other values (7) 51
 
4.0%
ValueCountFrequency (%)
1 5
 
0.4%
1.5 10
 
0.8%
2 61
 
4.8%
2.5 87
 
6.9%
3 245
19.3%
3.5 98
 
7.7%
4 235
18.5%
4.5 14
 
1.1%
5 217
17.1%
6 134
10.6%
ValueCountFrequency (%)
14 1
 
0.1%
13 1
 
0.1%
12 3
 
0.2%
10 17
 
1.3%
9 22
 
1.7%
8 42
 
3.3%
7 75
 
5.9%
6 134
10.6%
5 217
17.1%
4.5 14
 
1.1%

coil_size2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)1.3%
Missing53
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.3900329
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:30.211038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12
median3
Q34
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7209656
Coefficient of variation (CV)0.50765453
Kurtosis3.8192961
Mean3.3900329
Median Absolute Deviation (MAD)1
Skewness1.5501706
Sum4115.5
Variance2.9617225
MonotonicityNot monotonic
2023-09-16T15:35:30.363805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 246
19.4%
3 220
17.4%
4 199
15.7%
2.5 152
12.0%
5 108
8.5%
1.5 63
 
5.0%
6 61
 
4.8%
1 47
 
3.7%
3.5 40
 
3.2%
7 26
 
2.1%
Other values (6) 52
 
4.1%
(Missing) 53
 
4.2%
ValueCountFrequency (%)
1 47
 
3.7%
1.5 63
 
5.0%
2 246
19.4%
2.5 152
12.0%
3 220
17.4%
3.5 40
 
3.2%
4 199
15.7%
4.5 10
 
0.8%
5 108
8.5%
6 61
 
4.8%
ValueCountFrequency (%)
14 2
 
0.2%
12 1
 
0.1%
10 3
 
0.2%
9 15
 
1.2%
8 21
 
1.7%
7 26
 
2.1%
6 61
 
4.8%
5 108
8.5%
4.5 10
 
0.8%
4 199
15.7%

coil_length2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)2.5%
Missing53
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean7.1828666
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:30.525545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile20
Maximum50
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.4928616
Coefficient of variation (CV)0.76471719
Kurtosis7.2854042
Mean7.1828666
Median Absolute Deviation (MAD)2
Skewness2.3011839
Sum8720
Variance30.171528
MonotonicityNot monotonic
2023-09-16T15:35:30.697469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4 262
20.7%
6 205
16.2%
8 192
15.2%
3 137
10.8%
10 113
8.9%
2 107
8.4%
15 54
 
4.3%
20 31
 
2.4%
12 22
 
1.7%
30 19
 
1.5%
Other values (20) 72
 
5.7%
(Missing) 53
 
4.2%
ValueCountFrequency (%)
1 9
 
0.7%
2 107
8.4%
2.5 1
 
0.1%
3 137
10.8%
3.5 4
 
0.3%
4 262
20.7%
4.5 1
 
0.1%
5 9
 
0.7%
5.4 2
 
0.2%
6 205
16.2%
ValueCountFrequency (%)
50 1
 
0.1%
30 19
1.5%
26 1
 
0.1%
25 6
 
0.5%
24 6
 
0.5%
23 1
 
0.1%
21 6
 
0.5%
20 31
2.4%
18 3
 
0.2%
17 5
 
0.4%

coil_size3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)1.4%
Missing156
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean2.9050405
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:30.852039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2.5
Q34
95-th percentile6
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5434347
Coefficient of variation (CV)0.53129543
Kurtosis4.5658496
Mean2.9050405
Median Absolute Deviation (MAD)0.5
Skewness1.706152
Sum3227.5
Variance2.3821908
MonotonicityNot monotonic
2023-09-16T15:35:31.004773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 301
23.8%
3 173
13.7%
4 156
12.3%
2.5 132
10.4%
1.5 110
 
8.7%
1 81
 
6.4%
5 52
 
4.1%
6 41
 
3.2%
3.5 24
 
1.9%
8 16
 
1.3%
Other values (5) 25
 
2.0%
(Missing) 156
12.3%
ValueCountFrequency (%)
1 81
 
6.4%
1.5 110
 
8.7%
2 301
23.8%
2.5 132
10.4%
3 173
13.7%
3.5 24
 
1.9%
4 156
12.3%
4.5 3
 
0.2%
5 52
 
4.1%
6 41
 
3.2%
ValueCountFrequency (%)
14 1
 
0.1%
10 2
 
0.2%
9 4
 
0.3%
8 16
 
1.3%
7 15
 
1.2%
6 41
 
3.2%
5 52
 
4.1%
4.5 3
 
0.2%
4 156
12.3%
3.5 24
 
1.9%

coil_length3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)2.0%
Missing156
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean5.7007201
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-09-16T15:35:31.163252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile15
Maximum30
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.4851848
Coefficient of variation (CV)0.78677513
Kurtosis9.087181
Mean5.7007201
Median Absolute Deviation (MAD)2
Skewness2.6042174
Sum6333.5
Variance20.116882
MonotonicityNot monotonic
2023-09-16T15:35:31.476430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 249
19.7%
2 200
15.8%
3 174
13.7%
6 165
13.0%
8 145
11.4%
10 62
 
4.9%
15 33
 
2.6%
20 16
 
1.3%
12 13
 
1.0%
1 12
 
0.9%
Other values (12) 42
 
3.3%
(Missing) 156
12.3%
ValueCountFrequency (%)
1 12
 
0.9%
2 200
15.8%
3 174
13.7%
3.5 1
 
0.1%
4 249
19.7%
4.5 2
 
0.2%
5 7
 
0.6%
6 165
13.0%
7 5
 
0.4%
7.5 2
 
0.2%
ValueCountFrequency (%)
30 10
 
0.8%
25 3
 
0.2%
24 4
 
0.3%
20 16
 
1.3%
17 2
 
0.2%
15 33
2.6%
13 1
 
0.1%
12 13
 
1.0%
11 1
 
0.1%
10 62
4.9%

Interactions

2023-09-16T15:35:21.242842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:49.054452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:51.691342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:54.078773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:56.720564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:59.040407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:01.593374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:04.004564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:06.238532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:08.661685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:11.180937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:13.724841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:16.295175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:18.689349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:21.423959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:49.235626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:51.876760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:54.261151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:56.896027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:59.216688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:01.773224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:04.148188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:06.421658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:08.839242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:11.365638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:13.904762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:16.476390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:18.872785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:21.606494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:49.424720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:52.055275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:54.440184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:57.065913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:59.384413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:01.955373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:04.292874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:06.603970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:09.012891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:11.546815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:14.083454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:16.654957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:19.052920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:21.787976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:49.608958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:52.236996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:54.623355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:57.240309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:59.562060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:02.134708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:04.439305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:06.784515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:09.190088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:11.723523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:14.266725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:16.831555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:19.235233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:21.950044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:49.780474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:52.395853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:54.795126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:57.398459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:59.727354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:02.294724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:04.580868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:06.949466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:09.351964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:11.888651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:14.436795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:16.997613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:19.553364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:22.116904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:49.955457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:52.573442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:54.973214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:57.562037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:59.897427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:02.465793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:04.723110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:07.121063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:09.516253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:12.063588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:14.769401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:17.157289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:19.722154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:22.285570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:50.131921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:52.754043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:55.143600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:57.729838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:00.078814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:02.635404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:04.868621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:07.293444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:09.855019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:12.233959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:14.935811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:17.322834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:19.895795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:22.434832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:50.273984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:52.898623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:55.286736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:57.871147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:00.223458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:02.795885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:05.175389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:07.449738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:10.000672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:12.382259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:15.082064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:17.468494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:20.039365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:22.605483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:50.457172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:53.060200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:55.467043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:58.037524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:00.559108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:02.966481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:05.333501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:07.622822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:10.169279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:12.555724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:15.254482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:17.643596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:20.211119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:22.767693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:50.627735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:53.210561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:55.809381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:58.197065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:00.723380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:03.134006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:05.480465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:07.785348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:10.332067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:12.725318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:15.417499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:17.805105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:20.376449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:22.942522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:50.808684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:53.384747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:55.986880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:58.365673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:00.899879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:03.305091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:05.636809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:07.960548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:10.502535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:12.901467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:15.593325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:17.981381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:20.553060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:23.114309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:51.151327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:53.559153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:56.168975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:58.536659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:01.071739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:03.476567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:05.787000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:08.138958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:10.674845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:13.076191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:15.773808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:18.164487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:20.726450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:23.285776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:51.324017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:53.740994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:56.350534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:58.701431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:01.242126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:03.655297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:05.938760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:08.308446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:10.834859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:13.250713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:15.947039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:18.335493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:20.896419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:23.462416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:51.507274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:53.887970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:56.552531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:34:58.872136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:01.417505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:03.832300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:06.090410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:08.486435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:11.009239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:13.549945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:16.123131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:18.513258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-16T15:35:21.067521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-16T15:35:31.655588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IDAgeAneu_neckAneu_widthAneu_heightAneu_volumeVERcoil_countcoil_length1coil_size1coil_size2coil_length2coil_size3coil_length3SexAneu_locationAdj_techIs_blebAneu_width_label
ID1.0000.046-0.005-0.088-0.153-0.1250.1030.146-0.199-0.217-0.182-0.150-0.247-0.1600.0360.0930.3310.0960.076
Age0.0461.0000.1520.0860.0780.087-0.2440.0830.0670.0790.0630.0720.0240.0480.0930.0990.0600.0780.058
Aneu_neck-0.0050.1521.0000.6960.6020.712-0.1090.5520.5970.6210.6010.5990.5740.6020.0350.1660.2030.0510.442
Aneu_width-0.0880.0860.6961.0000.6960.965-0.1610.6520.7730.8120.7720.7500.7520.7510.0000.1050.1280.0730.894
Aneu_height-0.1530.0780.6020.6961.0000.852-0.2720.5610.7740.8210.7590.7400.7170.7180.0600.0960.0940.0000.506
Aneu_volume-0.1250.0870.7120.9650.8521.000-0.2080.6650.8290.8740.8270.8040.8040.8030.0140.1020.0830.0850.781
VER0.103-0.244-0.109-0.161-0.272-0.2081.0000.184-0.133-0.133-0.141-0.147-0.251-0.2320.0000.0000.0000.1650.070
coil_count0.1460.0830.5520.6520.5610.6650.1841.0000.5030.5610.5530.5220.5200.4990.0000.0650.1290.1160.370
coil_length1-0.1990.0670.5970.7730.7740.829-0.1330.5031.0000.9300.8310.8210.7710.7740.0000.0500.0860.0520.559
coil_size1-0.2170.0790.6210.8120.8210.874-0.1330.5610.9301.0000.8910.8540.8300.8150.0000.0780.0790.0150.635
coil_size2-0.1820.0630.6010.7720.7590.827-0.1410.5530.8310.8911.0000.9360.9100.8850.0080.1180.0500.0000.602
coil_length2-0.1500.0720.5990.7500.7400.804-0.1470.5220.8210.8540.9361.0000.8510.8750.0390.1010.0560.0000.522
coil_size3-0.2470.0240.5740.7520.7170.804-0.2510.5200.7710.8300.9100.8511.0000.9170.0000.1260.0590.0000.570
coil_length3-0.1600.0480.6020.7510.7180.803-0.2320.4990.7740.8150.8850.8750.9171.0000.0610.1180.0530.0000.561
Sex0.0360.0930.0350.0000.0600.0140.0000.0000.0000.0000.0080.0390.0000.0611.0000.2880.1030.0000.000
Aneu_location0.0930.0990.1660.1050.0960.1020.0000.0650.0500.0780.1180.1010.1260.1180.2881.0000.1950.0000.134
Adj_tech0.3310.0600.2030.1280.0940.0830.0000.1290.0860.0790.0500.0560.0590.0530.1030.1951.0000.0000.115
Is_bleb0.0960.0780.0510.0730.0000.0850.1650.1160.0520.0150.0000.0000.0000.0000.0000.0000.0001.0000.047
Aneu_width_label0.0760.0580.4420.8940.5060.7810.0700.3700.5590.6350.6020.5220.5700.5610.0000.1340.1150.0471.000

Missing values

2023-09-16T15:35:23.899249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T15:35:24.277059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-16T15:35:24.561998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDSexAgeAneu_locationAneu_neckAneu_widthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_countAneu_width_labelcoil_length1coil_size1coil_size2coil_length2coil_size3coil_length3
03027woman79BA3.07.010.0311.383333SimplenoNaN62.020.08.06.010.05.010.0
13030woman54ICA3.05.05.065.416667BATnoNaN21.06.03.02.02.0NaNNaN
23031man60ACA2.54.55.055.931250SimplenoNaN20.08.04.02.54.0NaNNaN
33035woman63ICA3.53.55.545.333750SimplenoNaN30.08.04.02.54.02.03.0
43037woman82ACA3.66.05.6101.987200SimplenoNaN31.010.05.04.08.02.56.0
53038woman72ICA2.53.04.021.980000SimplenoNaN40.06.03.02.03.02.02.0
63041woman71MCA2.03.84.031.023200SimplenoNaN30.06.04.02.56.02.04.0
73042man65ACA2.64.34.240.168450SimplenoNaN10.08.05.0NaNNaNNaNNaN
83044woman70ACA1.84.45.969.966527SimplenoNaN40.010.05.04.08.02.56.0
93045woman70VA3.35.24.253.719120SimplenoNaN41.010.05.04.08.02.56.0
IDSexAgeAneu_locationAneu_neckAneu_widthAneu_heightAneu_volumeAdj_techIs_blebVERcoil_countAneu_width_labelcoil_length1coil_size1coil_size2coil_length2coil_size3coil_length3
12574367woman68ICA2.603.692.9220.817770Stent assistnoNaN40.08.03.02.54.02.03.0
12584377woman53ACA3.003.654.1428.879169SimplenoNaN40.08.03.02.54.02.03.0
12594395woman58ICA2.273.992.6021.672937BATnoNaN40.08.03.03.06.02.04.0
12604396woman81MCA2.212.862.5610.964041Stent assistnoNaN20.04.02.51.03.0NaNNaN
12614400man62ACA1.992.752.6510.493247SimplenoNaN10.04.02.0NaNNaNNaNNaN
12624408man75ACA4.996.844.54111.215848Stent assistnoNaN131.020.06.05.010.04.512.0
12634414woman64ICA3.595.274.9471.836771Stent assistnoNaN81.010.05.03.04.02.04.0
12644424woman52MCA2.712.543.3111.181345Stent assistnoNaN60.04.02.52.04.01.03.0
12654431woman29ICA2.993.922.9924.057026Stent assistnoNaN20.06.03.02.04.0NaNNaN
12664434woman60VA6.007.508.50250.345665Stent assistnoNaN142.030.010.08.020.07.015.0